"Vacinômetro" passa a divulgar o número de vacinados por município

Saiba mais em www.vacinaja.sp.gov.br

Health Information Management Reimagined: Assessing Current Professional Skills and Industry Demand.

This paper examines the changes affecting the health information management (HIM) professional skill set and industry demand to determine differences affecting practitioners. As the industry continues to experience technological innovation, the responsibilities of the HIM professional are in flux, affecting the required skill set of the changing environment. This research used the American Health Information Management Association salary survey and current job postings to determine whether the workforce has experienced deskilling and whether a theory-practice-gap exists. It also assesses if industry competencies align with the Health Information Management Reimaged perspectives. The results indicate that the workforce has not experienced deskilling, that a theory-practice gap does exist, and that Health Information Management Reimaged is aligned with industry needs.

Evaluation of the malaria reporting system supported by the District Health Information System 2 in Solomon Islands.

BACKGROUND: District Health Information Systems 2 (DHIS2) is used for supporting health information management in 67 countries, including Solomon Islands. However, there have been few published evaluations of the performance of DHIS2-enhanced disease reporting systems, in particular for monitoring infectious diseases such as malaria. The aim of this study was to evaluate DHIS2 supported malaria reporting in Solomon Islands and to develop recommendations for improving the system. METHODS: The evaluation was conducted in three administrative areas of Solomon Islands: Honoria City Council, and Malaita and Guadalcanal Provinces. Records of nine malaria indicators including report submission date, total malaria cases, Plasmodium falciparum case record, Plasmodium vivax case record, clinical malaria, malaria diagnosed with microscopy, malaria diagnosed with (rapid diagnostic test) (RDT), record of drug stocks and records of RDT stocks from 1st January to 31st December 2016 were extracted from the DHIS2 database. The indicators permitted assessment in four core areas: availability, completeness, timeliness and reliability. To explore perceptions and point of view of the stakeholders on the performance of the malaria case reporting system, focus group discussions were conducted with health centre nurses, whilst in-depth interviews were conducted with stakeholder representatives from government (province and national) staff and World Health Organization officials who were users of DHIS2. RESULTS: Data were extracted from nine health centres in Honoria City Council and 64 health centres in Malaita Province. The completeness and timeliness from the two provinces of all nine indicators were 28.2% and 5.1%, respectively. The most reliable indicator in DHIS2 was 'clinical malaria' (i.e. numbers of clinically diagnosed malaria cases) with 62.4% reliability. Challenges to completeness were a lack of supervision, limited feedback, high workload, and a lack of training and refresher courses. Health centres located in geographically remote areas, a lack of regular transport, high workload and too many variables in the reporting forms led to delays in timely reporting. Reliability of reports was impacted by a lack of technical professionals such as statisticians and unavailability of tally sheets and reporting forms. CONCLUSION: The availability, completeness, timeliness and reliability of nine malaria indicators collected in DHIS2 were variable within the study area, but generally low. Continued onsite support, supervision, feedback and additional enhancements, such as electronic reporting will be required to further improve the malaria reporting system.

A COVID-19 e o papel dos sistemas de informação e das tecnologias na atenção primária / COVID-19 and the role of information systems and technologies at the first level of care / COVID-19 y el rol de los sistemas de información y las tecnologías en el primer nivel de atención

Atenção centrada na resposta à COVID-19: identificar, informar, conter, manejar e encaminhar. Os sistemas de informação em saúde ­ por meio do acesso oportuno a dados devidamente desagregados, a correta integração dos sistemas nacionais e locais, a saúde digital e o uso das tecnologias da informação (TIC) de uso frequente ­ facilitam a identificação eficaz, informação e análise de casos e contatos; a busca e detecção de casos em tempo hábil; e a identificação e seguimento da população de risco, dos casos e de seus contatos. A contenção é fortalecida com as plataformas de seguimento e monitoramento de casos, contatos, quarentena e isolamento social. Por sua vez, esses sistemas possibilitam a difusão maciça a toda a sociedade dos comunicados sobre medidas preventivas. As plataformas de teleconsulta, monitoramento remoto de pacientes e comunicação a distância permitem à atenção primária manejar a assistência médica e facilitam o seguimento domiciliar das pessoas com COVID-19. Esses mesmos mecanismos, integrados aos prontuários eletrônicos e aos sistemas locais e nacionais de informação, permitem e facilitam as referências, em âmbito hospitalar, dos pacientes com sinais e sintomas graves ou com fatores de risco.

Atención centrada en la respuesta a la COVID-19: identificar, reportar, contener, manejar y referir. Los sistemas de información para la salud ­a través del acceso oportuno a datos correctamente desagregados, la correcta integración de los sistemas nacionales y locales, la salud digital y la utilización de las tecnologías de la información (TIC) de uso frecuente­ facilitan la identificación eficaz, el reporte y análisis de casos y contactos; la búsqueda y detección tempranas de casos; y la identificación y el seguimiento de la población de riesgo, los casos y sus contactos. La contención se ve fortalecida con las plataformas de seguimiento y monitoreo de casos, contactos, cuarentena y aislamiento social. Estos sistemas permiten a su vez la difusión masiva a toda la sociedad de las comunicaciones sobre medidas preventivas. Las plataformas de teleconsulta, monitoreo remoto de pacientes y comunicación a distancia permiten al primer nivel de atención el manejo de la asistencia médica y facilitan el seguimiento domiciliario de las personas con COVID-19. Estos mismos mecanismos, integrados con los registros electrónicos de salud y los sistemas locales y nacionales de información, permiten y facilitan las referencias al nivel hospitalario de los pacientes con signos y síntomas graves o factores de riesgo.

Care centered on the response to COVID-19: Identify, report, contain, manage, and refer. Information systems for health­through timely access to correctly disaggregated data, proper integration of national and local systems, digital health, and the application of widely used information and communication technologies (ICTs)­facilitate the effective identification, reporting, and analysis of cases and contacts; early search for and detection of cases; and identification and monitoring of at-risk populations, cases, and contacts. Containment is strengthened through platforms for follow-up and monitoring of cases, contacts, quarantine, and social isolation. These systems, in turn, enable mass dissemination of information on preventive measures to all of society. Platforms for telemedicine visits, remote monitoring of patients, and remote communication enable health workers at the first level of care to manage medical care and facilitate home monitoring of people with COVID-19. These same mechanisms, together with electronic health records and local and national information systems, facilitate hospital referrals of patients with severe signs and symptoms or risk factors.

Por que a desagregação de dados é essencial durante pandemias / Por qué es importante la desagregación de datos durante una pandemia / Why Data Disaggregation is key during a pandemic

O que é desagregação de dados? O termo dados desagregados se refere à separação das informações coletadas em unidades menores para revelar tendências e padrões subjacentes. Os dados compilados podem vir de diversas fontes (setores público e privado e organizações nacionais e internacionais) e ter diversas variáveis, ou "dimensões". Para melhor entender uma situação, os dados são agrupados por dimensão, como idade, sexo, área geográfica, escolaridade, etnia ou outras variáveis socioeconômicas. Por que precisamos de dados desagregados durante uma pandemia? Quando ocorre uma pandemia, uma resposta adequada e eficiente requer a identificação e caracterização dos fatores que desaceleram ou aceleram a transmissão e das populações mais vulneráveis. Dados desagregados de alta qualidade, acessíveis, seguros, atuais, abertos e confiáveis são fundamentais a fim de gerar informações valiosas para a tomada de decisões em tempo real. Por exemplo, para determiner se uma intervenção (como a autotriagem em massa) é efetiva, precisamos saber a proporção da população que foi testada. Isso pode exigir análise por idade, área geográfica e/ou outras variáveis socioeconômicas...

¿Qué significa la desagregación de datos? La desagregación de datos se refiere a la separación de la información recabada en unidades más pequeñas para dilucidar las tendencias y los patrones subyacentes. Los datos recabados pueden provenir de múltiples fuentes (los sectores público y privado, y organizaciones nacionales e internacionales) y tener múltiples variables o "dimensiones". Para mejorar la comprensión de una situación, los datos se agrupan por dimensión, como edad, sexo, zona geográfica, educación, etnicidad u otras variables socioeconómicas. ¿Por qué necesitamos datos desagregados durante una pandemia? Cuando hay una pandemia, una respuesta apropiada y eficaz requiere que determinemos y caractericemos los factores que enlentecen o aceleran la transmisión y los grupos poblacionales que son más vulnerables. Los datos desagregados de alta calidad, accesibles, fiables, oportunos, abiertos y fidedignos son fundamentales para generar información valiosa para la toma de decisiones en tiempo real. Por ejemplo, a fin de determinar si una intervención (como el autotamizaje masivo) es eficaz, tenemos que saber qué proporción de la población ha sido objeto de la prueba. Esto puede requerir un análisis por edad, zona geográfica u otras variables socioeconómicas...

Data Disaggregation is the separation of compiled information into smaller units to elucidate underlying trends and patterns. High quality, accessible, trusted, timely, open, and reliable disaggregated data is critical to generating valuable information for decision-making in real time.

[Health record linkage: Andalusian health population database]. / Conexión de registros sanitarios: base poblacional de salud de Andalucía.

OBJECTIVE: To describe the development of an information system that connects data from multiple health records to improve assistance to patients, health services administration, management, evaluation, and inspection, as well as public health and research. METHOD: Deterministic connection of pseudonymized data from a population of 8.5 million inhabitants provided by: a users database, DIRAYA electronic medical records, minimum basic data sets (inpatients, outpatient mayor surgery, hospital emergencies and medical day hospital), mental health information systems, analytical and image tests, vaccines, renal patients, and pharmacy. An automatic coder was used to code clinical diagnoses and 80 chronic pathologies were identified to follow-up. The architecture of the information system consisted of three layers: data (Oracle Database 11g), applications (MicroStrategy BI) and presentation (MicroStrategy Web, JavaScript libraries, HTML 5 and CSS style sheets). Measures for the governance of the system were implemented. RESULTS: Data from 12.5 million health system users between 2001 and 2017 were gathered, including 435.5 million diagnoses, 88.7% of which were generated by the automatic coder. Data can be accessed through predefined reports or dynamic queries, both exportable to CSV files for processing outside the system. Expert analysts can directly access the databases and perform queries using SQL or directly treat the data with external tools. CONCLUSION: The work has shown that the connection of health records opens new possibilities for data analysis.

Factors influencing harmonized health data collection, sharing and linkage in Denmark and Switzerland: A systematic review.

INTRODUCTION: The digitalization of medicine has led to a considerable growth of heterogeneous health datasets, which could improve healthcare research if integrated into the clinical life cycle. This process requires, amongst other things, the harmonization of these datasets, which is a prerequisite to improve their quality, re-usability and interoperability. However, there is a wide range of factors that either hinder or favor the harmonized collection, sharing and linkage of health data. OBJECTIVE: This systematic review aims to identify barriers and facilitators to health data harmonization-including data sharing and linkage-by a comparative analysis of studies from Denmark and Switzerland. METHODS: Publications from PubMed, Web of Science, EMBASE and CINAHL involving cross-institutional or cross-border collection, sharing or linkage of health data from Denmark or Switzerland were searched to identify the reported barriers and facilitators to data harmonization. RESULTS: Of the 345 projects included, 240 were single-country and 105 were multinational studies. Regarding national projects, a Swiss study reported on average more barriers and facilitators than a Danish study. Barriers and facilitators of a technical nature were most frequently reported. CONCLUSION: This systematic review gathered evidence from Denmark and Switzerland on barriers and facilitators concerning data harmonization, sharing and linkage. Barriers and facilitators were strictly interrelated with the national context where projects were carried out. Structural changes, such as legislation implemented at the national level, were mirrored in the projects. This underlines the impact of national strategies in the field of health data. Our findings also suggest that more openness and clarity in the reporting of both barriers and facilitators to data harmonization constitute a key element to promote the successful management of new projects using health data and the implementation of proper policies in this field. Our study findings are thus meaningful beyond these two countries.

Data Quality Assessment in Emergency Medical Services: What Are the Stakeholders' Perspectives?

Introduction: Emergency care is usually conducted within limited time and with limited resources. During emergency care processes, data quality issues should be taken into account. The aim of this study was to assess the quality of emergency care data from the perspectives of different data stakeholders. Method: This survey study was conducted in 2017. In this research, the viewpoints of three groups of data stakeholders, including data producers, data collectors, and data consumers, were collected regarding data quality in emergency care services. Data were collected by using a standard information quality assessment questionnaire. Results: The mean values for each dimension of data quality were as follows: sound data (6.23), dependable data (6.28), useful data (6.30), and usable data (6.35), with 0 being the lowest possible score and 10 being the highest. The role gap analysis suggested a clear gap between data producers and data customers at the university level. Conclusion: Overall, data quality in emergency medical services was not at a high level. Although data quality was improving, the levels of data completeness, compatibility, and usability were low. To improve the usability of emergency medical service data, more attention should be paid to the dimensions of accuracy, completeness, and consistency of data sources.

What happens to migrant tuberculosis patients who are transferred out using a web-based system in China?

BACKGROUND: In China, internal migrants constitute one-fifth of tuberculosis (TB) patients registered for treatment in web-based TB information management system (TBIMS). Though China added a specific module in the web-based TBIMS in 2009, web-based transfer-out is not specifically recommended in the national guidelines. OBJECTIVE: In this country wide study among all registered migrant TB patients (2014-2015) that were transferred out using web-based TBIMS in China, to determine the i) timing of transfer-out in relation to period of treatment; ii) delay and attrition during transfer interval (between transfer-out and transfer-in); and iii) extent and risk factors for 'not evaluated' as the treatment outcome. METHODS: This was a cohort study involving review of web-based TBIMS data. Modified Poisson regression was used to build a predictive model for risk factors of 'not evaluated' as the treatment outcome. RESULTS: Among 7 284 patients, 5 900 (81.0%) were transferred out during the first two months after initiation of treatment or before treatment initiation and 7 088 (97.3%) patients had arrived at transfer-in unit. The median transfer interval was three (interquartile range: 0-14) days. Sixteen percent (1 176/7 284) patients had 'not evaluated' as their treatment outcome. 'Not evaluated' contributed to 66% of the unfavourable outcomes. Patients transferred from referral hospitals, migrated from out of prefecture, transferred out of prefecture, with sputum smear negative pulmonary TB, with TB pleurisy and with long delay between symptom onset and treatment initiation had significantly higher risk of 'not evaluated' as the outcome. CONCLUSION: Web-based transfer helped as the delay and attrition during the transfer interval was quite short and treatment outcomes of more than four-fifths of transferred out migrant TB patients were available with transfer-out BMU. Once strategies to address the independent predictors of 'not evaluated' treatment outcome are devised, China may consider mandatory use of web-based TBIMS for transferring out migrant TB patients.

A beginner's guide to avoiding Protected Health Information (PHI) issues in clinical research - With how-to's in REDCap Data Management Software.

Protecting personally identifiable information is important in clinical research. The authors, two faculty members involved in developing and implementing research infrastructure for a medical school, observed challenges novice researchers encountered in recognizing, collecting, and managing Protected Health Information (PHI) for clinical research. However, we had difficulty finding resources that provide practical strategies for novice clinical researchers for this topic. Common issues for beginners were: 1. Recognition of PHI, e.g. lack of recognition of 'indirect' PHI, i.e., that the combination of two or more non-PHI data types or other specific information could result in identifiable data requiring protection; 2. Collection of PHI, e.g., proposed collection of data not necessary for fulfillment of the project's objectives or potential inadvertent collection of PHI in free text response items; and 3. Management of PHI, e.g., proposed use of coding systems that directly included PHI, or proposed data collection techniques, electronic data storage, or software with inadequate protections. From these observations, the authors provide the following in this paper: 1. A brief review of the elements of PHI, particularly 'indirect' PHI; 2. Sample data management plans for common project types relevant to novice clinical researchers to ensure planning for data security; 3. Basic techniques for avoiding issues related to the collection of PHI, securing and limiting access to collected PHI, and management of released PHI; and 4. Methods for implementing these techniques in the Research Electronic Data Capture (REDCap) system, a commonly used and readily available research data management software system.

The Odds of Success: Predicting Registered Health Information Administrator Exam Success.

The purpose of this study was to craft a predictive model to examine the relationship between grades in specific academic courses, overall grade point average (GPA), on-campus versus online course delivery, and success in passing the Registered Health Information Administrator (RHIA) exam on the first attempt. Because student success in passing the exam on the first attempt is assessed as part of the accreditation process, this study is important to health information management (HIM) programs. Furthermore, passing the exam greatly expands the graduate's job possibilities because the demand for credentialed graduates far exceeds the supply of credentialed graduates. Binary logistic regression was utilized to explore the relationships between the predictor variables and success in passing the RHIA exam on the first attempt. Results indicate that the student's cumulative GPA, specific HIM course grades, and course delivery method were predictive of success.

Factors that impact Patient Web Portal Readiness (PWPR) among the underserved.

OBJECTIVE: Healthcare organizations in the US are increasingly using Patient Portals as a means to provide patients with partial access to their health records and thereby comply with the 'meaningful use' of Health Information Technology policy issued by the US federal government. Patient portals are used to not only provide access to parts of the health records such as lab results but also offer services such as customized educational materials and appointment scheduling. While prior studies examining the adoption rates of these patient portals have not offered consistent findings, many of the studies have reported limited adoption and use [1] of patient portals, especially among the underserved population. This study explores the factors behind the reduced adoption rate of patient portals among the underserved by focusing on their Patient Web Portal Readiness (PWPR). DESIGN: The study empirically evaluates the impact of three important variables on PWPR among the underserved: (a) Personal Health Information Management (PHIM) activities, (b) patient attitude toward personal health record keeping; and (c) use of Internet for health information seeking. The study also incorporates three other factors: (d) access to Internet; (e) demographics; and (f) presence of chronic illness. MEASUREMENTS: Data were collected through a survey from 132 patients from the underserved population who visited 5 free clinics in the Northern Virginia area in the US. The paper-based survey was administered to the patients who visited these free clinics for care. RESULTS: The study findings show support for the hypotheses related to the impact of the two key factors - Personal Health Information Management (PHIM) activities and attitude toward personal health record keeping - on PWPR. The findings also indicate that the use of Internet for health information seeking has relatively more impact than patient's Internet access on PWPR. Overall, the findings imply the critical importance of complementary activities - e.g., PHIM activities, Internet-based health information seeking - to enhance PWPR among the underserved population.

La integración de la información de salud del paciente / Integration of health information patient

Las tecnologías de la información y la comunicación (TIC) están implementadas en los sistemas de salud. Sin embargo, la actual situación socioeconómica plantea algunos interrogantes respecto a cómo evolucionará el sistema de salud en un contexto de uso generalizado de las TIC, pero con dificultades de sostenibilidad. Además de la sostenibilidad y la consolidación, ahora el reto planteado es la integración de la información y en qué ámbito asistencial y qué profesionales de la salud deberán liderar este paso adelante en la atención de salud de las personas de la comunidad. Todo ello supone un importante cambio de mentalidad para los usuarios de los sistemas de salud, y la necesidad de integrar todos los cuidados de salud, trabajando de manera más transversal, con el objetivo de dar continuidad asistencial y propiciando más calidad en la atención de salud del ciudadano (AU)

Information and communications technology (ICT) is implemented in health systems. However, the current economic situation raises questions regarding how the health system will evolve in a context of widespread use of ICT, but with sustainability problems. Beside sustainability and consolidation, now a days, the challenge is the integration of information and in which care level and who among health professionals should lead this step forward in health care. All this represents a major change of mindset for users of health systems, and the need to integrate all health care, working more transversely, with the aim of achieve more health care continuity and provide better quality in health care of citizen (AU)

Using 'big data' to validate claims made in the pharmaceutical approval process.

Big Data in the healthcare setting refers to the storage, assimilation, and analysis of large quantities of information regarding patient care. These data can be collected and stored in a wide variety of ways including electronic medical records collected at the patient bedside, or through medical records that are coded and passed to insurance companies for reimbursement. When these data are processed it is possible to validate claims as a part of the regulatory review process regarding the anticipated performance of medications and devices. In order to analyze properly claims by manufacturers and others, there is a need to express claims in terms that are testable in a timeframe that is useful and meaningful to formulary committees. Claims for the comparative benefits and costs, including budget impact, of products and devices need to be expressed in measurable terms, ideally in the context of submission or validation protocols. Claims should be either consistent with accessible Big Data or able to support observational studies where Big Data identifies target populations. Protocols should identify, in disaggregated terms, key variables that would lead to direct or proxy validation. Once these variables are identified, Big Data can be used to query massive quantities of data in the validation process. Research can be passive or active in nature. Passive, where the data are collected retrospectively; active where the researcher is prospectively looking for indicators of co-morbid conditions, side-effects or adverse events, testing these indicators to determine if claims are within desired ranges set forth by the manufacturer. Additionally, Big Data can be used to assess the effectiveness of therapy through health insurance records. This, for example, could indicate that disease or co-morbid conditions cease to be treated. Understanding the basic strengths and weaknesses of Big Data in the claim validation process provides a glimpse of the value that this research can provide to industry. Big Data can support a research agenda that focuses on the process of claims validation to support formulary submissions as well as inputs to ongoing disease area and therapeutic class reviews.

Informatics Competencies in Nursing Management.

This study aimed to identify in scientific literature the informatics competencies required from the nurses to make decision in management process. Through a scoping review, literature databases were searched to find articles published in Portuguese, English, or Spanish, until July 2013. 188 articles were found, and seven were included in this study, published between 1994 and 2011. The studies were written in English (5; 71%), in USA (5; 71%), using experience reports or literature review designs (5; 71%). The informatics competences were categorized according the Technology Informatics Guiding Education Reform (TIGER). The findings highlight gaps in informatics competence to make decisions in the management process--essentially in information management competence.

Anesthesia Information Management Systems: An Underutilized Tool for Outcomes Research.

Perioperative outcomes research using anesthesia information management systems (AIMS) is an emerging research method that has not been comprehensively reviewed. This review reports an initial analysis of the use of AIMS for perioperative patient outcomes research from articles published between January 1980 and January 2013. Perioperative patient outcomes research based on AIMS has increased greatly since 2001. Although risk stratification studies involving large study populations were most commonly reported, AIMS were also used to report a rare life-threatening anesthesia-related complication, malignant hyperthermia. Use of AIMS for perioperative outcomes research can address clinically relevant topics that traditional research methods have been unable to adequately address, mainly because of the innate challenges presented by perioperative anesthesia practice. It is expected that perioperative effectiveness and outcomes research using large AIMS databases will be more widely embraced in the future to generate useful evidence and knowledge to improve anesthesia care.